Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

Lê, Thu Trang; Atto, Abdourrahmane M.; Trouvé, Emmanuël; Solikhin, Akhmad; Pinel, Virginie

doi:10.1016/j.isprsjprs.2015.02.008

Cited by 42 publications

(22 citation statements)

References 44 publications

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“…PolSAR 1 images due to the interaction between electromagnetic waves and objects and having the phase and amplitude due to a different scattering mechanisms (surface, double-bounce, and volume scattering) have extra different information from the ground in different polarization (HH, HV, VH, and VV). However, these images because of the interaction of electromagnetic waves and objects at ground level include an inherent speckle noise (Lê et al, 2015). Radar imagery independent from weather condition and can penetrate in clouds and snow and can be operated day and night and these advantage covers the weaknesses of optical images (Lee and Pottier, 2009).…”

Section: Introductionmentioning

confidence: 99%

Land Cover Changes Detection in Polarimetric Sar Data Using Algebra, Similarity and Distance Based Methods

Najafi

Hasanlou

Akbari

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images.

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Section: Introductionmentioning

confidence: 99%

Land Cover Changes Detection in Polarimetric Sar Data Using Algebra, Similarity and Distance Based Methods

Najafi

Hasanlou

Akbari

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A simpler approach which still makes allowance for radar texture is proposed in [19]. Other methods proposed recently include extensions of the LRT to multitemporal [20] and multifrequency data [21], a patch based change detector which works on speckle filtered data [22], other objectoriented methods utilizing post-classification comparison [23], [24], and an approach to processing of time series of PolSAR data [25]. In this paper, we propose a simpler test statistic which still assumes the complex Wishart distribution for the covariance matrix data, and yet is able to detect changes in many scenarios.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric SAR Change Detection With the Complex Hotelling–Lawley Trace Statistic

Akbari

Anfinsen

Doulgeris

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is approximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test.

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“…Several methods were implemented to identify urban landuse/landcover classes. Some of these methods are supervised classification from backscatter and coherence (Parihar, Das, Rathore, Nathawat, & Mohan, 2014), unsupervised classification (Ince, 2010), object-oriented image analysis, change vector analysis, post-classification comparison (Biro et al, 2013;Qi et al, 2015), change detection matrix (Lê, Atto, Trouvé, Solikhin, & Pinel, 2015), polarimetric decomposition, Pol-SAR interferometry, and decision tree algorithms (Qi, Yeh, Li, & Lin, 2012). Fusion of optical and SAR images also proved to be a useful method in urban landuse/landcover classification.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel – 1 Images

Roychowdhury¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

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Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

Cited by 42 publications

References 44 publications

Land Cover Changes Detection in Polarimetric Sar Data Using Algebra, Similarity and Distance Based Methods

Land Cover Changes Detection in Polarimetric Sar Data Using Algebra, Similarity and Distance Based Methods

Polarimetric SAR Change Detection With the Complex Hotelling–Lawley Trace Statistic

Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel – 1 Images

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